Azure Data Analyst Portfolio

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Repository: azure-data-analyst-portfolio
A practical, job-focused portfolio showcasing data analysis skills using Microsoft Azure, Power BI, SQL, and Python.

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🎯 Goal

This portfolio demonstrates my ability to:

• Collect, clean, and model data
• Write clear, efficient SQL for analysis
• Build Power BI dashboards with meaningful KPIs
• Communicate insights in a business-friendly way
• Apply Azure fundamentals to modern analytics workflows

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🧰 Core Skills

• Power BI: Data modeling, DAX, measures, interactive dashboards
• SQL: Joins, aggregations, window functions, KPI queries
• Python: Pandas-based analysis, cleaning, EDA, visualisation
• Azure: Data fundamentals and cloud-ready analytics concepts
• Reporting: KPI definition, stakeholder-ready summaries

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📜 Certifications (Azure Track)

• DP-900 — Microsoft Azure Data Fundamentals (targeted)
• PL-300 — Microsoft Power BI Data Analyst Associate (targeted)
• DP-203 — Microsoft Azure Data Engineer Associate (future)
• DP-100 — Microsoft Azure Data Scientist Associate (future)

Note: This repository is structured to grow from Data Analyst → Analytics Engineer → Data/ML roles over time.

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📂 Repository Structure (Overview)

azure-data-analyst-portfolio/
│
├── 00_README_ASSETS/ # Images used in README (dashboards, diagrams)
├── 01_PowerBI_Dashboards/ # Power BI projects (PBIX + screenshots)
├── 02_SQL_Analysis/ # SQL queries & analysis tasks
├── 03_Python_Analysis/ # Jupyter notebooks for EDA & cleaning
├── 04_Azure_Data_Samples/ # Azure-flavoured demos (optional)
├── 05_Reports/ # PDF/MD executive-style summaries
└── datasets/ # Public or synthetic datasets (where allowed)

📂 Repository Structure (Detailed)

azure-data-analyst-portfolio/
│
├── README.md
│
├── datasets/
│   ├── README.md
│   └── sample_datasets/
│
├── 01_powerbi_dashboards/
│   ├── README.md
│   ├── sales_performance_dashboard/
│   │   ├── sales_dashboard.pbix
│   │   └── screenshots/
│   │       ├── overview.png
│   │       └── kpis.png
│   └── customer_insights_dashboard/
│
├── 02_sql_analysis/
│   ├── README.md
│   ├── sales_analysis.sql
│   ├── customer_retention.sql
│   └── README_queries.md
│
├── 03_python_analysis/
│   ├── README.md
│   ├── eda_sales.ipynb
│   ├── data_cleaning.ipynb
│   └── utils/
│
├── 04_azure_data_projects/
│   ├── README.md
│   ├── azure_sql_to_powerbi/
│   │   ├── architecture.png
│   │   └── setup_notes.md
│
├── 05_reports/
│   ├── README.md
│   ├── executive_summary.md
│   └── sales_insights_report.pdf
│
└── .gitignore

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✅ Featured Projects

1) Power BI — KPI Dashboard (PL-300 aligned)

Location: 01_PowerBI_Dashboards/
What it shows:

• Data modeling (star schema where applicable)
• Key measures (DAX): revenue, trends, YoY/MoM, segmentation
• Clean visual storytelling + drill-down navigation

Deliverables:

• .pbix file
• Dashboard screenshots
• 1-page insight summary (Markdown/PDF)

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2) SQL — Business Questions & KPI Queries

Location: 02_SQL_Analysis/
What it shows:

• Querying for insights: sales, retention, churn, cohorts
• Intermediate SQL: joins, CTEs, window functions
• Clean query formatting and documentation

Deliverables:

• .sql scripts
• Query outputs (sample tables)
• Short insight notes

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3) Python — Data Cleaning + Exploratory Data Analysis (EDA)

Location: 03_Python_Analysis/
What it shows:

• Reproducible analysis in notebooks
• Cleaning pipelines (missing values, duplicates, outliers)
• EDA charts and insights using Pandas + Matplotlib

Deliverables:

• .ipynb notebooks
• Exported figures
• Summary of findings

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☁️ Azure Component (Optional but Recommended)

Location: 04_Azure_Data_Samples/
Examples of Azure-based workflows (as I build them):

• Azure Storage (Blob) → Power BI
• Azure SQL Database → Power BI
• Basic pipeline concepts aligned with DP-900 / DP-203

This section focuses on simple, realistic, junior-friendly Azure scenarios.

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🧭 How to Navigate This Repo

• Start with Power BI dashboards: 01_PowerBI_Dashboards/
• Review SQL scripts: 02_SQL_Analysis/
• Explore Python notebooks: 03_Python_Analysis/
• Read executive-style summaries: 05_Reports/

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🧪 Tools & Tech

• Power BI Desktop
• SQL (Azure SQL / PostgreSQL / SQL Server syntax depending on project)
• Python (Pandas, NumPy, Matplotlib)
• Git / GitHub
• Azure concepts (AZ-900 / DP-900 aligned)

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📈 What I’m Building Next

• A complete Power BI project with dataset + DAX measures + report narrative
• A SQL mini-pack with 10 business questions + clean solutions
• A Python EDA pack with a reusable cleaning template
• A simple Azure SQL → Power BI pipeline demo

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📌 Notes on Datasets

• Datasets are either public, open-license, or synthetic.
• If a dataset has licensing restrictions, I will include:
  • Source link
  • Licensing note
  • Instructions to download independently

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🤝 Contact / Collaboration

If you’re a recruiter or hiring manager and would like:

• a quick walkthrough of a project,
• a Power BI demo,
• or a summary of how I built a dashboard end-to-end,

feel free to reach out via GitHub.

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Last updated: January 2026

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🙌 Acknowledgments

Built and maintained by Georgios Giannakopoulos.
Inspired by open knowledge engineering and long-term documentation practices.

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📜 License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0).